Actual Output Signal Research Articles

Switching periodic event‐triggered global prescribed performance control of uncertain strict‐feedback systems with sensor faults

AbstractThis study investigates the global adaptive prescribed performance control (PPC) of a class of uncertain strict‐feedback nonlinear systems with sensor faults based on the switching periodic event‐triggering mechanism (SPETM). Due to the existence of sensor faults, the controlled system is first remodeled by utilizing the available variables. To reduce the communication frequency, a novel SPETM is proposed by combining the advantages of the static and the dynamic event‐triggering mechanisms. This mechanism can not only avoid continuously monitoring the event‐triggering condition and avoid the Zeno phenomenon in mechanism, but also reduce the trigger frequency while ensuring the system performance by adjusting the event‐triggering threshold dynamically. Meanwhile, a time‐varying scaling function, whose reciprocal is considered as a prescribed performance function, is designed to achieve the global PPC by combining the nonlinear transformation technique. The adaptive control algorithm design is completed by employing the backstepping methodology, which can guarantee that all closed‐loop signals are bounded and the actual system output signal evolves within the prescribed performance boundary for arbitrary initial values. The effectiveness and the advantages of the proposed control algorithm are illustrated through an application example of the network‐based robotic manipulator system.

A surrogate model for finite element simulation of elastic wave propagation in composite materials

Deep learning surrogate models can be employed in solid mechanics to forecast the behavior of structures subjected to various loading conditions, substantially decreasing the computational costs associated with simulations. In this letter, we have utilized convolutional neural networks and Fourier transform to predict the elastic wave output from composite bars. The microstructures of the bar are utilized as inputs to the deep learning model, while the output is the elastic wave response. The convolutional neural network learns to identify crucial input composite features and utilizes this information to predict the output elastic waves. Finally, the mean squared error of the predicted output signals is compared to the actual output signals, which was used to evaluate the model. The outcomes of this study demonstrate that the deep learning model can precisely and swiftly predict the output elastic waves of the composites, thus serving as a surrogate model for time-consuming finite element simulations.

Track Image Acquisition Based on the Signal Device of the Photoelectric Encoder

The photoelectric encoder is a kind of encoder that uses high-precision metrology grating as the detection element to convert the input angular position information into corresponding digital signals through the photo-electric conversion device. It has the advantages of high resolution and wide measuring range, so it is widely used in precision angle measuring devices and high-precision speed regulating systems. In this study, an incremental photoelectric encoder is used, and a receiving system of vertical incidence is chosen, covering a photocell receiving circuit and an infrared circuit. The signal acquisition device of the photoelectric encoder is further designed, that is, the weak photoelectric signal is captured and transformed into an electrical signal that can be recognized by the microprocessor after amplification and adjustment, and then the signal is processed and transmitted. In the test, the signal output of the photoelectric encoder is simulated first. The results reveal that the theoretical output voltage of the drive is 3.5 V, and the peak of the actual output signal is 7.2 V, which meets the design requirements. The designed photoelectric encoder signal device is used with the charge coupled device (CCD) camera to capture the track image. The single-camera and dual-camera tests are carried out respectively. The speed of the bearing vehicle is adjusted to 0.6 m/s~1.2 m/s, and the velocity of the vehicle is adjusted timely. The findings show that the signal device of the photoelectric encoder +CCD camera can be triggered in real-time with the different speeds of the vehicle, and obtain relatively clear images of the track surface and its image of the fastener.

Characterization of a resonant capacitively coupled wireless power transfer system for communication purposes at 6 MHz

This work characterizes a wireless power transmission (WPT) system operating at the 6 MHz frequency band for wireless information transfer purposes. To perform the characterization, the channel is modelled using its 2-port network scattering parameters. Then, a long string of symbols encoded with on–off keying and modified Miller encoding is passed through the channel model at various data rates. The original data is recovered from the channel output signal using a software-defined receiver. Bit error rate curves and eye diagrams are created for each encoding scheme and data rate. To verify the simulated results, the aforementioned data strings are transferred through the actual WPT system and output signals are post-processed in the digital domain with the same software-defined receiver. For comparison purposes, the performance of the WPT channel is compared to that of a conventional wireless communication channel with a flat additive white Gaussian noise model. The simulation and measurement results demonstrate that the provided WPT system can be used for data transfer with an acceptable error rate.

Learning control for discrete-time nonlinear systems with sensor saturation and measurement noises

ABSTRACTThe iterative learning control (ILC) is investigated for a class of nonlinear systems with measurement noises where the output is subject to sensor saturation. An ILC algorithm is introduced based on the measured output information rather than the actual output signal. A decreasing sequence is also incorporated into the learning algorithm to ensure a stable convergence under stochastic noises. It is strictly proved with the help of the stochastic approximation technique that the input sequence converges to the desired input almost surely along the iteration axis. Illustrative simulations are exploited to verify the effectiveness of the proposed algorithm.

Theoretical analysis and finite element method simulations on dynamic sensitivity of hemisphere-shaped electrostatic sensors

Electrostatic sensors are key components of electrostatic monitoring systems. Their sensitivity characteristics have a direct influence on monitoring accuracy. In previous studies, spatial sensitivity, which is called static sensitivity here, was used to describe the sensitivity characteristics. However, it only reflects a basic relationship between static charged particles and induced charges on an electrostatic sensor’s probe. Besides, as a three-dimensional defined parameter, it is difficult to build a unified model if actual boundary conditions are considered. Thus, it is not quite proper for applications that detect moving particles. To solve this problem, dynamic sensitivity is proposed in this article. As for a hemisphere-shaped electrostatic sensor, first, a more accurate model of static sensitivity is built. Based on it, dynamic sensitivity is defined and modeled analytically. Then, a calibration method is proposed to improve the model’s accuracy under actual boundary conditions. In the end, finite element method simulations are done for validations. The results demonstrate that dynamic sensitivity reflects a relationship between moving charged particles and the actual output signals of a sensor, thus it is direct and practical for moving particles. And the theoretical results are highly consistent with the simulated ones. Moreover, the dynamic sensitivity indicates localized sensing characteristics of hemisphere-shaped electrostatic sensors.

Dynamic characteristics and control capability of a piezostack actuator at high temperatures: experimental investigation

In this study, experiments are conducted to evaluate dynamic characteristics and position tracking control performance of a piezoelectric actuator at various temperature conditions including up to 180 °C. An experimental apparatus consisting of a heat chamber, piezostack actuator, a laser sensor, a gap sensor, a temperature sensor, a data acquisition board, a function generator and a computer is established. To obtain the dynamic characteristics of a commercial piezostack actuator, desired input signals, which are sinusoidal waveforms with several different frequencies, are generated by the function generator, and actual controlled output signals are detected by the laser sensor. In this experiment, the heat chamber regulates temperature conditions of the piezostack actuator for a sufficient time before starting next test. After discussing the temperature dependent dynamic properties such as blocking force, another experimental setup is established to evaluate control performance of the piezostack actuator at high temperatures. A proportional-integral-derivative feedback controller which does not require an exact dynamic model of the system is designed and experimentally realized using a microprocessor for the position tracking control. Control performances such as position tracking error are measured at various temperatures and presented in time domain.

Optimal observation of controlled elastic vibrations of a beam in the presence of measurement errors

The problem of constructing of an optimal operation for restoring the state of controlled elastic vibrations of a beam in the presence of measurement errors is investigated. By the method of separation of variables, the problem is reduced to an observation problem with an actual output signal for an infinite system of ordinary differential equations. For each harmonic, a universal optimal operation that restores the deflection of the beam from equilibrium and the velocities of all points of the beam is constructed by amplifying the ideal part of the signal produced by the system.

On Evaluation of Beamforming Networks

An approach is presented to characterize a beamforming network by means of a single quantity. It is based on a comparison of the actual output signal vector to the desired output signal vector. The formulation can be concisely written in matrix notation and allows for a characterization over frequency, accounting for absolute offsets. By expressing the characteristics of a beamforming network by a single quantity, subsequent iterations of one network or different network implementations can be compared to each other. The formulation is applied to the evaluation of a 4 × 4 Butler matrix implemented in three different ways.

DEVELOPMENT OF INTEGRATED MICROFLUIDIC DEVICE FOR OPTICAL FLOW RATE SENSING

The design, fabrication and characterization of an optics based integrated flow rate sensor is presented where the light-fluid interaction is maximized by allowing the liquid and light to propagate along the same direction. The flow rate sensor consists of a 10 μm deep microchannel placed between two waveguides. The optical waveguides were tapered to fit the channel width, to guide light in and out of the microchannel. A tapering mechanism is proposed to minimize the coupling and propagation losses. The power of the output signal from the designed device was calculate through simulation and it was compared with the actual output signal detected by a fast receiver (higher than 1 MHz). The dynamic change of the light intensity when fluid flows through the channel can also be recorded by this receiver. This scheme allows for a direct measurement of the liquid flow rate with higher interaction length between fluid and light with a dynamic range of up to 0.18. An integrated microfluidic device with high precision and sufficient coupling between the light source and the microfluidic channel is proposed.

Balance Platform Vibration Control

In the presented paper the problem of balance platform control was considered. The object used during research is the original structure, designed by article authors. The work was divided into three parts: the identification process, control system design and laboratory tests. In the first stage of the identification process a model describing the system was build, using the measurement data recorded on input and output system. The model was built without the knowledge of the mechanisms that occur in the process; only based on the relationship between the measured data. Next the estimation was performed for selection of suitable algorithm, and determination of the parameters of model selected in the previous step. The last step was to verify and to check the results of identification process. By comparing the signals obtained in response to a signal given from the model of a registered object actual output signal, it is possible to visually estimate the accuracy of the identification and designation of the error. After identification process the control system was designed. In this application the PID controller was chosen. It was used to balance platform position and velocity control. The last part of the article shows the results of various laboratory tests and comparison of quality control.

Balance Platform Model Parameter Identification

In the first stage of the identification process was to build a model describing the system, using the measurement data recorded on input and output system [. The model was built without the knowledge of the mechanisms that occur in the process, only based on the relationship between the collected data. As an input parameter the defined type of motion was used, next converted to digital form by the real-time computer and sent to the axes control card. While the output signal was registered as a change of encoders position. Sampling time 20 ms was adopted. For each axis separately recorded measurements (signals were with different amplitudes and frequencies of motion tape device) [. In the next step the estimation was performed for the selection suitable algorithm, and the determination the parameters of model selected in the previous step [. The last step was to verify, to check the results of identification process. By comparing the signals obtained in response to a signal given the model of a registered object actual output signal, it is possible to visually estimate the accuracy of the identification and designation of the error. The last part of the article shows the results of various tests.

SELF-OSCILLATING CLASS D AMPLIFICATION DEVICE

An amplification device, comprising a device input receiving a device input signal, an amplifier unit comprising a zero crossing detector unit, an output filter and a lead-lag compensation network. The zero crossing detector unit compares the device input signal with a reference potential and switches a pulse width modulated detector output signal between first and second voltage levels dependent on the comparison. The amplifier unit provides an actual device output signal, e.g., an amplified representation of the device input signal. The amplification device further comprises a device output providing the actual device output signal, a control loop bridging the amplifier unit and comprising a forward filter, e.g., an integrating filter, for increasing loop gain improving the signal-to-noise ratio of the actual device output signal, and a deviation detection unit detecting over modulation of the amplifier unit, so that the amplification device disables forward filter functioning upon over modulation.

Real-time multi-rate HIL simulation platform for evaluation of a jet engine fuel controller

A new Hardware-In-the-Loop (HIL) platform is developed for testing of a turbojet engine fuel control system using a multi-rate simulation platform. The HIL equipment consists of an industrial PC and a commercial I/O board for jet engine simulation as the controlled process and an Electronic Control Unit (ECU) as the fuel controller. The controlled process consisting of actuator, physical process and sensors is fully simulated in HIL simulation. However, the high resolution signals of some components in the HIL simulation cause the real-time simulation to become difficult due to the need of small time-steps. As a result, the disparity between the jet engine model sampling rate and these high resolution signals requires a multi-rate simulation. In this study, a multi step size simulation is developed using multiple processors. These processors are designed to synchronize the status of the engine model with the control system as well as to convert the raw data of the I/O boards to actual input and output signals in real-time. These features make the HIL equipment more effective and flexible. The HIL environment is proved to be an efficient tool to develop various control functions and to validate the software and hardware of the engine fuel control system.

Applying the Oscillation Test Strategy to FPAA’s Configurable Analog Blocks

This paper presents the application of the oscillation test methodology as an alternative to test configurable analog blocks of Field Programmable Analog Arrays. The blocks of the device under test are first configured to behave as oscillators. Then, the output frequency and amplitude are observed to obtain the signature of the fault-free circuit. During test, this signature is compared to the actual output signal. Experimental results show the effectiveness of the method in detecting parametric and large deviation faults of the tested components.

A cost- and time-effective hardware-in-the-loop simulation platform for automotive engine control systems

A new PC-based hardware-in-the-loop simulation (HILS) platform is developed for designing an automotive engine control system. The HILS equipment consists of a widely used PC and commercial off-the-shelf (COTS) I/O boards instead of a powerful computing system and custom-made I/O boards. These features make the HILS equipment more cost effective and flexible. The HILS uses an automatic code generation extension, REAL-TIME WORKSHOP® of the MATLAB® tool-chain, which is one of the standard tools for modelling and off-line simulation in the area of controller design. This helps the control system developers to handle the controlled-object model more easily and to test the control system more comfortably and time effectively. The mean value engine model, which is used in the control design phase, is imported in this HILS. The engine model is supplemented with some I/O subsystems and I/O boards to interface actual input and output signals in real time. The I/O subsystems are designed to synchronize the status of the engine model with the control system as well as to convert the raw data of the I/O boards to the appropriate forms for proper interfaces. To prove the feasibilities of the proposed environment, a pilot project for the development of an air-to-fuel ratio control system is carried out. The HILS environment is proved to be an efficient tool to develop various control functions and to validate the software and hardware of the engine control system.

Modified optimization layer by layer algorithm for learning multilayer perceptrons

Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learning examples (training patterns), is as small as possible. The backpropagation, or variations thereof, is a standard method applied to adjust the synaptic weights in the network in order to minimize a given cost function. However as a steepest descent approach, BP algorithm is too slow for many applications.

Data processing of reflection seismic data by use of neural network

The present paper proposes an algorithm for data processing of reflection seismic data using of neural networks. A neural network algorithm was applied to the reading of arrival time of first break signal, the recognition of waveform in seismic trace and the automatic picking of result of constant velocity scan among the various data processing techniques. A layered network with the correct answer, so called, teacher's signal, in training period by the error back propagation algorithm was used. The general procedure of processing of reflection seismic data by use of neural network is as follows. 1. 1. Constitution of the most suitable network for the target processing. 2. 2. Setting of the weight values to all units in the layers and the teacher's signal. 3. 3. Calculation of the output signals from the output layer by activating the network. 4. 4. Estimation of learning signal from the energy of errors between the actual output signal and the teacher's signal. 5. 5. Calculation of the change of weight values by using learning signal so as to minimize the energy of errors between the actual signal and the teacher's signal. 6. 6. Steps (3) to (5) are repeated till the errors fall into the designated limitation or the designated learning count is reached. As a result of model studies, it was determined that the proposed algorithm performed the readings of arrival time of first break signal, the waveform recognition and the automatic picking of velocity analysis result with good accuracy.

Method of determining the dynamic characteristics of the sensing elements of pressure gages

This article examines features of a method developed to determine the dynamic characteristics of the sensing elements of pressure gages. The error realized when the signal is replaced by a δ-function or discontinuity is taken into account and an algorithm is created to determine the dynamic characteristics from an analysis of the actual output signal.

Method and apparatus for calibrating a keyboard

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